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Statistical Methods in HEP, in particular... Nuisance parameters and systematic uncertainties

Statistical Methods in HEP, in particular... Nuisance parameters and systematic uncertainties. Glen Cowan Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan RHUL HEP Seminar 22 March, 2006

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Statistical Methods in HEP, in particular... Nuisance parameters and systematic uncertainties

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  1. Statistical Methods in HEP, in particular...Nuisance parameters and systematic uncertainties Glen Cowan Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan RHUL HEP Seminar 22 March, 2006 A rehash my talk at the IoP Half Day Meeting on Statistics in HEP University of Manchester, 16 November, 2005 Itself a rehash of PHYSTAT 2005, Oxford, September 2005 Glen Cowan RHUL HEP seminar, 22 March, 2006

  2. Vague outline I. Nuisance parameters and systematic uncertainty II. Parameter measurement Frequentist Bayesian III. Estimating intervals (setting limits) Frequentist Bayesian IV. Comment on the D0 result on Bs mixing V. Conclusions Glen Cowan RHUL HEP seminar, 22 March, 2006

  3. Statistical vs. systematic errors Statistical errors: How much would the result fluctuate upon repetition of the measurement? Implies some set of assumptions to define probability of outcome of the measurement. Systematic errors: What is the uncertainty in my result due to uncertainty in my assumptions, e.g., model (theoretical) uncertainty; modelling of measurement apparatus. The sources of error do not vary upon repetition of the measurement. Often result from uncertain value of, e.g., calibration constants, efficiencies, etc. Glen Cowan RHUL HEP seminar, 22 March, 2006

  4. Systematic errors and nuisance parameters Response of measurement apparatus is never modelled perfectly: y (measured value) model: truth: x (true value) Model can be made to approximate better the truth by including more free parameters. systematic uncertainty ↔ nuisance parameters Glen Cowan RHUL HEP seminar, 22 March, 2006

  5. Nuisance parameters Suppose the outcome of the experiment is some set of data values x (here shorthand for e.g. x1, ..., xn). We want to determine a parameter q, (could be a vector of parameters q1, ..., qn). The probability law for the data x depends on q : L(x| q) (the likelihood function) E.g. maximize L to find estimator Now suppose, however, that the vector of parameters: contains some that are of interest, and others that are not of interest: Symbolically: The are called nuisance parameters. Glen Cowan RHUL HEP seminar, 22 March, 2006

  6. Example #1: fitting a straight line Data: Model: measured yi independent, Gaussian: assume xi and si known. Goal: estimate q0 (don’t care about q1). Glen Cowan RHUL HEP seminar, 22 March, 2006

  7. Case #1: q1 known a priori For Gaussian yi, ML same as LS Minimize c2→estimator Come up one unit from to find Glen Cowan RHUL HEP seminar, 22 March, 2006

  8. Case #2: both q0 and q1 unknown Standard deviations from tangent lines to contour Correlation between causes errors to increase. Glen Cowan RHUL HEP seminar, 22 March, 2006

  9. Case #3: we have a measurement t1 of q1 The information on q1 improves accuracy of Glen Cowan RHUL HEP seminar, 22 March, 2006

  10. The profile likelihood The ‘tangent plane’ method is a special case of using the profile likelihood: is found by maximizing L (q0, q1) for each q0. Equivalently use The interval obtained from is the same as what is obtained from the tangents to Well known in HEP as the ‘MINOS’ method in MINUIT. Profile likelihood is one of several ‘pseudo-likelihoods’ used in problems with nuisance parameters. See e.g. talk by Rolke at PHYSTAT05. Glen Cowan RHUL HEP seminar, 22 March, 2006

  11. The Bayesian approach In Bayesian statistics we can associate a probability with a hypothesis, e.g., a parameter value q. Interpret probability of q as ‘degree of belief’ (subjective). Need to start with ‘prior pdf’ p(q), this reflects degree of belief about q before doing the experiment. Our experiment has data x, → likelihood functionL(x|q). Bayes’ theorem tells how our beliefs should be updated in light of the data x: Posterior pdf p(q|x) contains all our knowledge about q. Glen Cowan RHUL HEP seminar, 22 March, 2006

  12. Case #4: Bayesian method We need to associate prior probabilities with q0 and q1, e.g., reflects ‘prior ignorance’, in any case much broader than ←based on previous measurement Putting this into Bayes’ theorem gives: posterior Q likelihood  prior Glen Cowan RHUL HEP seminar, 22 March, 2006

  13. Bayesian method (continued) We then integrate (marginalize) p(q0, q1 | x) to find p(q0 | x): In this example we can do the integral (rare). We find Ability to marginalize over nuisance parameters is an important feature of Bayesian statistics. Glen Cowan RHUL HEP seminar, 22 March, 2006

  14. Digression: marginalization with MCMC Bayesian computations involve integrals like often high dimensionality and impossible in closed form, also impossible with ‘normal’ acceptance-rejection Monte Carlo. Markov Chain Monte Carlo (MCMC) has revolutionized Bayesian computation. Google for ‘MCMC’, ‘Metropolis’, ‘Bayesian computation’, ... MCMC generates correlated sequence of random numbers: cannot use for many applications, e.g., detector MC; effective stat. error greater than √n . Basic idea: sample multidimensional look, e.g., only at distribution of parameters of interest. Glen Cowan RHUL HEP seminar, 22 March, 2006

  15. MCMC basics: Metropolis-Hastings algorithm Goal: given an n-dimensional pdf generate a sequence of points Proposal density e.g. Gaussian centred about 1) Start at some point 2) Generate 3) Form Hastings test ratio 4) Generate move to proposed point 5) If else old point repeated 6) Iterate Glen Cowan RHUL HEP seminar, 22 March, 2006

  16. Metropolis-Hastings (continued) This rule produces a correlated sequence of points (note how each new point depends on the previous one). For our purposes this correlation is not fatal, but statistical errors larger than naive The proposal density can be (almost) anything, but choose so as to minimize autocorrelation. Often take proposal density symmetric: Test ratio is (Metropolis-Hastings): I.e. if the proposed step is to a point of higher , take it; if not, only take the step with probability If proposed step rejected, hop in place. Glen Cowan RHUL HEP seminar, 22 March, 2006

  17. Metropolis-Hastings caveats Actually one can only prove that the sequence of points follows the desired pdf in the limit where it runs forever. There may be a “burn-in” period where the sequence does not initially follow Unfortunately there are few useful theorems to tell us when the sequence has converged. Look at trace plots, autocorrelation. Check result with different proposal density. If you think it’s converged, try it again with 10 times more points. Glen Cowan RHUL HEP seminar, 22 March, 2006

  18. Example: posterior pdf from MCMC Sample the posterior pdf from previous example with MCMC: Summarize pdf of parameter of interest with, e.g., mean, median, standard deviation, etc. Although numerical values of answer here same as in frequentist case, interpretation is different (sometimes unimportant?) Glen Cowan RHUL HEP seminar, 22 March, 2006

  19. Case #5: Bayesian method with vague prior Suppose we don’t have a previous measurement of q1 but rather some vague information, e.g., a theorist tells us: q1≥ 0 (essentially certain); q1 should have order of magnitude less than 0.1 ‘or so’. Under pressure, the theorist sketches the following prior: From this we will obtain posterior probabilities for q0 (next slide). We do not need to get the theorist to ‘commit’ to this prior; final result has ‘if-then’ character. Glen Cowan RHUL HEP seminar, 22 March, 2006

  20. Sensitivity to prior Vary () to explore how extreme your prior beliefs would have to be to justify various conclusions (sensitivity analysis). Try exponential with different mean values... Try different functional forms... Glen Cowan RHUL HEP seminar, 22 March, 2006

  21. Example #2: Poisson data with background Count n events, e.g., in fixed time or integrated luminosity. s = expected number of signal events b = expected number of background events n ~ Poisson(s+b): Sometimes b known, other times it is in some way uncertain. Goal: measure or place limits on s, taking into consideration the uncertainty in b. Widely discussed in HEP community, see e.g. proceedings of PHYSTAT meetings, Durham, Fermilab, CERN workshops... Glen Cowan RHUL HEP seminar, 22 March, 2006

  22. Setting limits Frequentist intervals (limits) for a parameter s can be found by defining a test of the hypothesized value s (do this for all s): Specify values of the data n that are ‘disfavoured’ by s (critical region) such that P(n in critical region) ≤g for a prespecified g, e.g., 0.05 or 0.1. (Because of discrete data, need inequality here.) If n is observed in the critical region, reject the value s. Now invert the test to define a confidence interval as: set of s values that would not be rejected in a test of sizeg (confidence level is 1 - g ). The interval will cover the true value of s with probability ≥ 1 - g. Equivalent to Neyman confidence belt construction. Glen Cowan RHUL HEP seminar, 22 March, 2006

  23. Setting limits: ‘classical method’ E.g. for upper limit on s, take critical region to be low values of n, limit sup at confidence level 1 -b thus found from Similarly for lower limit at confidence level 1 - a, Sometimes choose a = b = g /2→ central confidence interval. Glen Cowan RHUL HEP seminar, 22 March, 2006

  24. Likelihood ratio limits (Feldman-Cousins) Define likelihood ratio for hypothesized parameter value s: Here is the ML estimator, note Critical region defined by low values of likelihood ratio. Resulting intervals can be one- or two-sided (depending on n). (Re)discovered for HEP by Feldman and Cousins, Phys. Rev. D 57 (1998) 3873. Glen Cowan RHUL HEP seminar, 22 March, 2006

  25. Nuisance parameters and limits In general we don’t know the background b perfectly. Suppose we have a measurement of b, e.g., bmeas ~ N (b, b) So the data are really: n events and the value bmeas. In principle the confidence interval recipe can be generalized to two measurements and two parameters. Difficult and rarely attempted, but see e.g. talk by G. Punzi at PHYSTAT05. G. Punzi, PHYSTAT05 Glen Cowan RHUL HEP seminar, 22 March, 2006

  26. Bayesian limits with uncertainty on b Uncertainty on b goes into the prior, e.g., Put this into Bayes’ theorem, Marginalize over b, then use p(s|n) to find intervals for s with any desired probability content. Controversial part here is prior for signal s(s) (treatment of nuisance parameters is easy). Glen Cowan RHUL HEP seminar, 22 March, 2006

  27. Cousins-Highland method Regard b as ‘random’, characterized by pdf (b). Makes sense in Bayesian approach, but in frequentist model b is constant (although unknown). A measurement bmeas is random but this is not the mean number of background events, rather, b is. Compute anyway This would be the probability for n if Nature were to generate a new value of b upon repetition of the experiment with b(b). Now e.g. use this P(n;s) in the classical recipe for upper limit at CL = 1 - b: Result has hybrid Bayesian/frequentist character. Glen Cowan RHUL HEP seminar, 22 March, 2006

  28. ‘Integrated likelihoods’ Consider again signal s and background b, suppose we have uncertainty in b characterized by a prior pdf b(b). Define integrated likelihood as also called modified profile likelihood, in any case not a real likelihood. Now use this to construct likelihood ratio test and invert to obtain confidence intervals. Feldman-Cousins & Cousins-Highland (FHC2), see e.g. J. Conrad et al., Phys. Rev. D67 (2003) 012002 and Conrad/Tegenfeldt PHYSTAT05 talk. Calculators available (Conrad, Tegenfeldt, Barlow). Glen Cowan RHUL HEP seminar, 22 March, 2006

  29. Interval from inverting profile LR test Suppose we have a measurement bmeas of b. Build the likelihood ratio test with profile likelihood: and use this to construct confidence intervals. See PHYSTAT05 talks by Cranmer, Feldman, Cousins, Reid. Glen Cowan RHUL HEP seminar, 22 March, 2006

  30. Comment on Bs mixing from D0 Last week D0 announced the discovery of Bs mixing: Moriond talk by Brendan Casey, also hep-ex/0603029 Produce a Bq meson at time t=0; there is a time dependent probability for it to decay as an anti-Bq (q = d or s): |Vts|À |Vtd| and so Bs oscillates quickly compared to decay rate Sought but not seen at LEP; early on predicted to be visible at Tevatron Here are some of Casey’s slides with commentary... Glen Cowan RHUL HEP seminar, 22 March, 2006

  31. Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

  32. Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

  33. Confidence interval from likelihood function In the large sample limit it can be shown for ML estimators: (n-dimensional Gaussian, covariance V) defines a hyper-ellipsoidal confidence region, If then Glen Cowan RHUL HEP seminar, 22 March, 2006

  34. Approximate confidence regions from L( ) So the recipe to find the confidence region with CL = 1- is: For finite samples, these are approximate confidence regions. Coverage probability not guaranteed to be equal to 1- ; no simple theorem to say by how far off it will be (use MC). Remember here the interval is random, not the parameter. Glen Cowan RHUL HEP seminar, 22 March, 2006

  35. Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

  36. Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

  37. Upper limit from test of hypothesized ms Base test on likelihood ratio (here  = ms): Observed value is lobs , sampling distribution is g(l;) (from MC) is excluded at CL=1- if D0 shows the distribution of ln l for ms = 25 ps-1 equivalent to 2.1 effect 95% CL upper limit Glen Cowan RHUL HEP seminar, 22 March, 2006

  38. Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

  39. Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

  40. Wrapping up I’ve shown a few ways of treating nuisance parameters in two examples (fitting line, Poisson mean with background). No guarantee this will bear any relation to the problem you need to solve... At recent PHYSTAT meetings the statisticians have encouraged physicists to: learn Bayesian methods, don’t get too fixated on coverage, try to see statistics as a ‘way of thinking’ rather than a collection of recipes. I tend to prefer the Bayesian methods for systematics but still a very open area of discussion. Glen Cowan RHUL HEP seminar, 22 March, 2006

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